Interactive Visuals as Metaphors for Dance Movement Qualities
Bibliographic record
Abstract
The notion of “movement qualities” is central in contemporary dance; it describes the manner in which a movement is executed. Movement qualities convey information revealing movement expressiveness; their use has strong potential for movement-based interaction with applications in arts, entertainment, education, or rehabilitation. The purpose of our research is to design and evaluate interactive reflexive visuals for movement qualities. The theoretical basis for this research is drawn from a collaboration with the members of the international dance company Emio Greco|PC to study their formalization of movement qualities. We designed a pedagogical interactive installation called Double Skin/Double Mind (DS/DM) for the analysis and visualization of movement qualities through physical model-based interactive renderings. In this article, we first evaluate dancers’ perception of the visuals as metaphors for movement qualities. This evaluation shows that, depending on the physical model parameterization, the visuals are capable of generating dynamic behaviors that the dancers associate with DS/DM movement qualities. Moreover, we evaluate dance students’ and professionals’ experience of the interactive visuals in the context of a dance pedagogical workshop and a professional dance training. The results of these evaluations show that the dancers consider the interactive visuals to be a reflexive system that encourages them to perform, improves their experience, and contributes to a better understanding of movement qualities. Our findings support research on interactive systems for real-time analysis and visualization of movement qualities, which open new perspectives in movement-based interaction design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".